Video data is continuously collected and stored by various imaging systems. These systems include various imaging systems deployed on satellites and unmanned aerial vehicles (UAVs) used in intelligence surveillance reconnaissance (ISR) applications, for example. In order to select desired portions of the data for viewing and/or analysis, the video data may be annotated and/or indexed for retrieval based on certain aspects of the video content.
In the past, video databases have been relatively small, and indexing and retrieval have been based on keywords annotated manually. Due to the increase of video database, content-based indexing and retrieval is required.
Due to the large amount of video data that is being captured and stored by various imaging systems, automatic systems and methods are needed to perform content-based video indexing and retrieval with minimum human intervention. Such systems may request and collect ISR sensor data and may automatically search, retrieve, and/or populate data, for example.
Automatic video indexing and retrieval techniques are generally based on particular representations or formats of the video data. Video information may be represented either as raw data or in compressed data formats, such as MPEG-4. Some of these video formats are not designed for video retrieval. Therefore, the process of retrieving desired portions of the video data is often complicated and slow. Moreover, retrieval results from data that is not indexed generally cannot be re-used.
Efficient techniques for video data storage and retrieval may rely heavily on video structure analysis. However, currently used video formats generally do not prepare video data for video structure analysis.
Certain video retrieval techniques that are presently being developed are limited to rapid retrieval based on the existing video representation, such as MPEG-4 or raw video.